scholarly journals Learning Complex Representations from Spatial Phase Statistics of Natural Scenes

2017 ◽  
Author(s):  
HaDi MaBouDi ◽  
Krishna Subramani ◽  
Hamid Soltanian-Zadeh ◽  
Shun-ichi Amari ◽  
Hideaki Shimazaki
Keyword(s):  
Phase Structure ◽  
Simulated Data ◽  
Natural Scenes ◽  
Phase Information ◽  
Likelihood Principle ◽  
Complex Signals ◽  
Spatial Phase ◽  
Quadratic Phase ◽  
Proposed Model ◽  
Complex Valued

AbstractNatural scenes contain higher-order statistical structures that can be encoded in their spatial phase information. Nevertheless, little progress has been made in modeling phase information of images, and understanding efficient representation of the image phases in the brain. In order to capture spatial phase structure under the efficient coding hypothesis, here we introduce a generative model of natural scenes by assuming independent source signals in a complex domain and non-uniform phase priors for the complex signals. Parameters of the proposed model are then estimated under the maximum-likelihood principle. This approach extends existing methods of independent component analysis for complex-valued signals to the one that utilizes phase information. Using simulated data, we demonstrate that the proposed model outperforms conventional models with a uniform phase prior in blind source separation of complex-valued signals. We then apply the proposed model to natural scenes in the Fourier domain. Real and imaginary parts of the learned complex features exhibit a pair of Gabor-like filters in quadratic phase structure with a similar shape. The proposed model significantly improved the goodness-of-the-fit from the model with a uniform phase prior, indicating that the structured spatial phases are important for removing redundancy in natural scenes. These results predict the presence of phase sensitive complex cells in the visual cortex.

scholarly journals Representation of higher-order statistical structures in natural scenes via spatial phase distributions

2016 ◽  
Vol 120 ◽  
pp. 61-73 ◽  
Author(s):  
HaDi MaBouDi ◽  
Hideaki Shimazaki ◽  
Shun-ichi Amari ◽  
Hamid Soltanian-Zadeh
Keyword(s):  
Higher Order ◽  
Natural Scenes ◽  
Spatial Phase

Messages of Oscillatory Correlograms: A Spike Train Model

2008 ◽  
Vol 20 (5) ◽  
pp. 1211-1238 ◽  
Author(s):  
Gaby Schneider
Keyword(s):  
Spike Train ◽  
Null Model ◽  
Simulated Data ◽  
Joint Analysis ◽  
Data Set ◽  
Proposed Model ◽  
Peak Asymmetry ◽  
Millisecond Range ◽  
Temporal Interactions ◽  
Underlying Processes

Oscillatory correlograms are widely used to study neuronal activity that shows a joint periodic rhythm. In most cases, the statistical analysis of cross-correlation histograms (CCH) features is based on the null model of independent processes, and the resulting conclusions about the underlying processes remain qualitative. Therefore, we propose a spike train model for synchronous oscillatory firing activity that directly links characteristics of the CCH to parameters of the underlying processes. The model focuses particularly on asymmetric central peaks, which differ in slope and width on the two sides. Asymmetric peaks can be associated with phase offsets in the (sub-) millisecond range. These spatiotemporal firing patterns can be highly consistent across units yet invisible in the underlying processes. The proposed model includes a single temporal parameter that accounts for this peak asymmetry. The model provides approaches for the analysis of oscillatory correlograms, taking into account dependencies and nonstationarities in the underlying processes. In particular, the auto- and the cross-correlogram can be investigated in a joint analysis because they depend on the same spike train parameters. Particular temporal interactions such as the degree to which different units synchronize in a common oscillatory rhythm can also be investigated. The analysis is demonstrated by application to a simulated data set.

On the Rapid Generation of Complete XRF Spectra for Material Analysis from Fundamental Parameters

2021 ◽  
Vol 105 ◽  
pp. 110-118
Author(s):  
Jie Si Ma ◽  
Fu Sheng Li ◽  
Yan Chun Zhao
Keyword(s):  
Simulated Data ◽  
Target Material ◽  
Mcnp Code ◽  
X Ray ◽  
Fundamental Parameters ◽  
Elemental Compositions ◽  
Statistical Precision ◽  
Proposed Model ◽  
Time Calculation ◽  
Rapid Generation

X-ray Fluorescence (XRF) analysis technology is used widely to detect and measure elemental compositions of target samples. The MCNP code developed by LANL can be utilized to simulate and generate the XRF spectrum of any sample with various elemental compositions. However, one shortcoming of MCNP code is that it takes quite a lot of time (in hours or longer) to generate one XRF spectrum with reasonable statistical precision; the other shortcoming is that MCNP code cannot produce L shell spectrum accurately. In this paper, a new computation model based on the Sherman equation (i.e., Fundamental Parameters, FP) is proposed to overcome the drawbacks of the MCNP code. The most important feature of this model is to achieve a full and accurate generation of spectral information of each element in a target material very rapidly (in seconds or less), including both K and L shell spectral peaks. Furtherly, it is demonstrated that the simulated data by this new mode match the experimental data very well. It proves that the proposed model can be a better alternative of MCNP code in the application of generation the XRF spectra of many materials, in terms of speed and accuracy. The proposed model can perform the simulation of XRF spectra in situ both fast and accurately, which is essential for real-time calculation of chemical composition by use of X-ray spectrometer, especially for those trace elements in target materials.

Natural-Image Discrimination in the Periphery: The Importance of Phase and Amplitude Information

Perception ◽  
1996 ◽  
Vol 25 (1_suppl) ◽  
pp. 85-85
Author(s):  
G M Kennedy ◽  
D J Tolhurst
Keyword(s):  
Phase Change ◽  
Scaling Factor ◽  
Natural Images ◽  
Natural Image ◽  
Rapid Decline ◽  
Natural Scenes ◽  
Phase Identification ◽  
Phase Information ◽  
Black And White ◽  
Sinusoidal Gratings

Previous studies with simplified stimuli such as combinations of sinusoidal gratings have revealed phase identification losses in the periphery that are not eliminated by a scaling factor. How do these phase processing problems influence our ability to discriminate natural images in the periphery? In this study the ability of an observer to identify the ‘odd-image-out’ when there is either an amplitude-only, phase-only, or amplitude and phase change in one out of three stimuli is compared. Pairs of Fourier-manipulated black-and-white digitised photographs of natural images were used and phase and amplitude spectral exchanges of varying proportions were made between two different images. Measurements were made to determine the smallest phase change needed in order for the observer to reliably discriminate the manipulated image, compared to two reference stimuli, at eccentricities of 0°, 2.5°, 5°, and 10°. This was compared to discrimination thresholds found when amplitude and phase, and amplitude alone were exchanged. The ability to discriminate images on the basis of phase information alone did fall off quickly with eccentricity (comparable to phase and amplitude discriminations). However, there was a much more rapid decline in amplitude-only discrimination. It appears that phase information in natural scenes remains a relatively more important visual cue in the periphery than amplitude.

A fuzzy rumor spreading model based on transmission capacity

2018 ◽  
Vol 29 (02) ◽  
pp. 1850012 ◽  
Author(s):  
Yi Zhang ◽  
Jiuping Xu ◽  
Yue Wu
Keyword(s):  
Simulated Data ◽  
Real Data ◽  
Transmission Capacity ◽  
Model Assessment ◽  
Rumor Spreading ◽  
Factors Affecting ◽  
Fuzzy Variables ◽  
Spreading Model ◽  
Proposed Model ◽  
Special Cases

This paper proposes a rumor spreading model that considers three main factors: the event importance, event ambiguity, and the publics critical sense, each of which are defined by decision makers using linguistic descriptions and then transformed into triangular fuzzy numbers. To calculate the resultant force of these three factors, the transmission capacity and a new parameter category with fuzzy variables are determined. A rumor spreading model is then proposed which has fuzzy parameters rather than the fixed parameters in traditional models. As the proposed model considers the comprehensive factors affecting rumors from three aspects rather than examining special factors from a particular aspect. The proposed rumor spreading model is tested using different parameters for several different conditions on BA networks and three special cases are simulated. The simulation results for all three cases suggested that events of low importance, those that are only clarifying facts, and those that are strongly critical do not result in rumors. Therefore, the model assessment results were proven to be in agreement with reality. Parameters for the model were then determined and applied to an analysis of the 7.23 Yong–Wen line major transportation accident (YWMTA). When the simulated data were compared with the real data from this accident, the results demonstrated that the interval for the rumor spreading key point in the model was accurate, and that the key point for the YWMTA rumor spread fell into the range estimated by the model.

scholarly journals Modeling virus spread on a network using NetLogo for optimum network management

2021 ◽  
Vol 23 (1) ◽  
pp. 370
Author(s):  
Catherine R. Alimboyong
Keyword(s):  
Computer Networks ◽  
Social Networking Sites ◽  
Simulated Data ◽  
Data Sets ◽  
Virus Propagation ◽  
Chi Square ◽  
Proposed Model ◽  
Accurate Performance ◽  
Simulated Data Sets ◽  
Network Administrators

<span>The infections in computer networks are complex. Its spread is analogous to a contagious disease which can cause destruction within a few seconds. Viruses in a computer or computer networks can spread rapidly by various means such as access to online social networking sites like twitter, Facebook, and opening of email attachments.  Thus, infections can go from being little dangerous to significantly harmful for a network. This paper proposed a simulation model that can predict the propagation of virus including the trend and the average infection rate using NetLogo software. Observed and simulated data sets were validated using chi-square tests. Results of the experiment have demonstrated accurate performance of the proposed model. The model could be very helpful for network administrators in mitigating the virus propagation and obstruct the spread of computer virus other than the usual prevention scheme particularly the use of antivirus software and inclusion of firewall security. </span>

scholarly journals Complex Neutrosophic Hypergraphs: New Social Network Models

Algorithms ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 234 ◽  
Author(s):  
Anam Luqman ◽  
Muhammad Akram ◽  
Florentin Smarandache
Keyword(s):  
Social Networks ◽  
Sustainable Development ◽  
Social Network ◽  
Complex Systems ◽  
Network Models ◽  
Neutrosophic Set ◽  
Neutrosophic Sets ◽  
Score Functions ◽  
Proposed Model ◽  
Complex Valued

A complex neutrosophic set is a useful model to handle indeterminate situations with a periodic nature. This is characterized by truth, indeterminacy, and falsity degrees which are the combination of real-valued amplitude terms and complex-valued phase terms. Hypergraphs are objects that enable us to dig out invisible connections between the underlying structures of complex systems such as those leading to sustainable development. In this paper, we apply the most fruitful concept of complex neutrosophic sets to theory of hypergraphs. We define complex neutrosophic hypergraphs and discuss their certain properties including lower truncation, upper truncation, and transition levels. Furthermore, we define T-related complex neutrosophic hypergraphs and properties of minimal transversals of complex neutrosophic hypergraphs. Finally, we represent the modeling of certain social networks with intersecting communities through the score functions and choice values of complex neutrosophic hypergraphs. We also give a brief comparison of our proposed model with other existing models.

scholarly journals Statistical Learning of the Worst Regional Smog Extremes with Dynamic Conditional Modeling

Atmosphere ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 665
Author(s):  
Lu Deng ◽  
Mengxin Yu ◽  
Zhengjun Zhang
Keyword(s):  
Statistical Learning ◽  
Weather Factors ◽  
Likelihood Principle ◽  
Modeling And Analysis ◽  
Pm 2.5 ◽  
Proposed Model ◽  
Autoregressive Parameter ◽  
Exponentiated Weibull Distribution ◽  
Exponentiated Weibull ◽  
Conditional Maximum

This paper is concerned with the statistical learning of the extreme smog (PM 2.5 ) dynamics of a vast region in China. Differently from classical extreme value modeling approaches, this paper develops a dynamic model of conditional, exponentiated Weibull distribution modeling and analysis of regional smog extremes, particularly for the worst scenarios observed in each day. To gain higher modeling efficiency, weather factors will be introduced in an enhanced model. The proposed model and the enhanced model are illustrated with temporal/spatial maxima of hourly PM 2.5 observations each day from smog monitoring stations located in the Beijing–Tianjin–Hebei geographical region between 2014 and 2019. The proposed model performs more precisely on fittings compared with other previous models dealing with maxima with autoregressive parameter dynamics, and provides relatively accurate prediction as well. The findings enhance the understanding of how severe extreme smog scenarios can be and provide useful information for the central/local government to conduct coordinated PM 2.5 control and treatment. For completeness, probabilistic properties of the proposed model were investigated. Statistical estimation based on the conditional maximum likelihood principle is established. To demonstrate the estimation and inference efficiency of studies, extensive simulations were also implemented.

scholarly journals The Half-Logistic Lomax Distribution for Lifetime Modeling

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Masood Anwar ◽  
Jawaria Zahoor
Keyword(s):  
Maximum Likelihood ◽  
Residual Life ◽  
Information Matrix ◽  
Simulated Data ◽  
Quantile Function ◽  
Rate Function ◽  
Mean Residual Life ◽  
Data Sets ◽  
Estimation Of Parameters ◽  
Proposed Model

We introduce a new two-parameter lifetime distribution called the half-logistic Lomax (HLL) distribution. The proposed distribution is obtained by compounding half-logistic and Lomax distributions. We derive some mathematical properties of the proposed distribution such as the survival and hazard rate function, quantile function, mode, median, moments and moment generating functions, mean deviations from mean and median, mean residual life function, order statistics, and entropies. The estimation of parameters is performed by maximum likelihood and the formulas for the elements of the Fisher information matrix are provided. A simulation study is run to assess the performance of maximum-likelihood estimators (MLEs). The flexibility and potentiality of the proposed model are illustrated by means of real and simulated data sets.

Using informative Multinomial-Dirichlet prior in a t-mixture with reversible jump estimation of nucleosome positions for genome-wide profiling

2015 ◽  
Vol 14 (6) ◽  
Author(s):  
Rawane Samb ◽  
Khader Khadraoui ◽  
Pascal Belleau ◽  
Astrid Deschênes ◽  
Lajmi Lakhal-Chaieb ◽  
...  
Keyword(s):  
Simulated Data ◽  
Reversible Jump ◽  
Control Of Gene Expression ◽  
Monte Carlo Simulation Technique ◽  
Genome Wide ◽  
Proposed Model ◽  
Heuristic Strategy ◽  
And Control ◽  
Physical Access

AbstractGenome-wide mapping of nucleosomes has revealed a great deal about the relationships between chromatin structure and control of gene expression. Recent next generation CHIP-chip and CHIP-Seq technologies have accelerated our understanding of basic principles of chromatin organization. These technologies have taught us that nucleosomes play a crucial role in gene regulation by allowing physical access to transcription factors. Recent methods and experimental advancements allow the determination of nucleosome positions for a given genome area. However, most of these methods estimate the number of nucleosomes either by an EM algorithm using a BIC criterion or an effective heuristic strategy. Here, we introduce a Bayesian method for identifying nucleosome positions. The proposed model is based on a Multinomial-Dirichlet classification and a hierarchical mixture distributions. The number and the positions of nucleosomes are estimated using a reversible jump Markov chain Monte Carlo simulation technique. We compare the performance of our method on simulated data and MNase-Seq data from Saccharomyces cerevisiae against PING and NOrMAL methods.

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